Refractive Surgery

Non-Neural Post-Thyroidectomy Dysphonia: Long-Term Voice Outcomes in 527 Patients.

OBJECTIVE: To characterize non-neural post-thyroidectomy dysphonia (PTD) by analyzing long-term voice outcomes in patients with no evidence of nerve injury on postoperative laryngoscopy and electromyography (EMG). STUDY DESIGN: Retrospective cohort study. SETTING: Tertiary referral center. METHODS: We reviewed 527 post-thyroidectomy patients with intact vocal fold mobility and normal EMG results a...

OCT-based myopic index: a biological predictor for the progression of high myopia.

BACKGROUND: The growth of axial length (AL) can lead to high myopia and ocular deformation, especially causing microstructural changes in the fundus, which cannot be fully quantified by AL alone. We propose an optical coherence tomography (OCT)-based modified AL (Myopic Index) to represent the extent of fundus deformation caused by AL elongation and to explore its clinical significance in myopic p...

Risk prediction modelling of 30-day all-cause mortality following percutaneous coronary intervention in an Australian population: leveraging machine learning.

BACKGROUND: Preprocedural risk prediction of 30-day all-cause mortality after percutaneous coronary intervention (PCI) aids in clinical decision-makin...

Predicting Electrolyte Stability in Lithium and Post-Lithium Metal Batteries: The Failure of Linear Single Descriptor-Based Models.

A stable solid electrolyte interphase (SEI), formed by the reductive decomposition of electrolytes at the anode surface, is crucial for ensuring the s...

Proof-of-concept: Differentiating upper trapezius muscle with myofascial trigger point using deep learning model on a small prospective sEMG dataset.

Myofascial pain syndrome commonly arises from myofascial trigger points (MTrPs). While needle electromyography (iEMG) reveals spontaneous activity at ...

Inferring preoperative cognitive function from intraoperative electroencephalography in elderly patients using machine learning.

To develop and evaluate machine learning (ML) models that infer preoperative cognitive function from intraoperative electroencephalography (EEG). This...

Motion capture dataset of 137 post-operative total hip replacement patients performing activities of daily living.

Three-dimensional motion capture is a powerful tool in clinical and engineering applications, but it can be time consuming, expensive, and difficult t...

AI-guided surgical decision support system for corneal laceration repair: a prospective, non-randomized controlled feasibility study.

To evaluate the feasibility and application value of a transfer learning-based artificial intelligence (AI) system for wound recognition and suture po...

Predicting VRE Infection After Liver Transplantation With a Time-Updated Colonization Score.

BACKGROUND: Vancomycin-resistant Enterococcus (VRE) infection is a life-threatening complication after liver transplantation (LT). This study aimed to...

Artificial intelligence-derived electrocardiographic age gap as a predictor of mortality after coronary revascularization: prognostic value and short-term intra-patient variability.

AIMS: The artificial intelligence (AI)-derived electrocardiographic (ECG) age gap-the difference between AI-predicted ECG age and chronological age-is...

Application of treatment response assessment maps (TRAMs), based on delayed-contrast MRI for radiomic characterization of breast lesions.

Dynamic contrast-enhanced (DCE) breast MRI is a highly sensitive modality for detecting breast cancer, but its limited specificity often leads to fals...

Artificial intelligence in oral and maxillofacial surgery: a scoping review of clinical applications, ethical challenges, and legal considerations.

Artificial intelligence (AI) is increasingly used in oral and maxillofacial surgery (OMS) for imaging, planning, and robotic/navigation support, yet e...

Examining the Impact of Optical Aberrations to Image Classification and Object Detection Models.

Deep neural networks (DNNs) have proven to be successful in various computer vision applications such that models even infer in safety-critical situat...

Exploring factors and models to predict post-dialysis volume overload status in maintenance hemodialysis patients based on pre-dialysis parameters.

OBJECTIVE: This study explored factors and models to predict post-dialysis volume overload status in maintenance hemodialysis patients (MHD) based on ...

Predicting seizure-free outcomes in people with treatment-resistant epilepsy: A machine learning approach.

BACKGROUND: Epilepsy surgery is an important intervention for treatment-resistant epilepsy, butthe ability to predict long-term seizure freedom post-s...

Distinguishing post-COVID from long-COVID in adults: Development and validation of a biomarker signature using targeted proteomics and machine learning in a cross-sectional observational study.

BACKGROUND: COVID-19 can have diverse clinical manifestations, ranging from asymptomatic infection to critical illness with multiorgan involvement. Wh...

Neuroimaging-Based Deep Learning Applications for Lesion Detection and Predicting the Outcome Following Epilepsy Surgery.

Neuroimaging studies are essential for evaluating patients with drug-resistant focal epilepsy and determining their candidacy for epilepsy surgery. Th...

Identification of extracellular matrix stiffness-related genes in human scleral fibroblasts through bioinformatics analysis and machine learning.

BACKGROUND: Scleral extracellular matrix (ECM) remodeling plays a key role in myopia development. This study aims to identify ECM stiffness-related ge...

Using Data and Artificial Intelligence to Improve Care Pathways of Older People Experiencing Falls and Frailty: Opportunities, Challenges, and Practical Considerations for Clinicians.

Older people living with falls and frailty are common in emergency attendances, admissions and functional decline. Artificial intelligence (AI) and ma...

Exploring key risk factors for loss to follow-up after hospitalization for acute stroke.

BACKGROUND: A significant proportion of stroke patients are lost to follow-up (LTFU) after discharge, which may increase risks of morbidity, mortality...